The document outlines the software and hardware requirements for detecting lung cancer using SVM classification and comparing patient survival rates through a 3D CNN on lung nodule datasets. Key software requirements include Ubuntu or Windows 10, Python with specific libraries, and TensorFlow, while hardware requirements specify a multi-core CPU, at least 8 GB RAM, a powerful GPU, and SSD storage. It emphasizes the need for advanced tools like Jupyter Notebook or Google Colab for data analysis and visualization.

1. Detection of Lung cancer from CT image using
SVM classification and compare the survival rate
of patients using 3D Convolutional neural
network (3D CNN) on lung nodules data set.
Title

2. 1. Operating System: Ubuntu 20.04 LTS or higher, or Windows 10 with WSL
2.
2. Python: Python 3.11.4, with support for numpy, pandas, matplotlib, scikit-
learn, and tensorflow-gpu.
3. Deep Learning Framework: TensorFlow 2.6 or higher, with support for
Keras and GPU acceleration.
4. Data Analysis Tools: Jupyter Notebook or Google Colab for data
exploration and visualization.
5. Tools : Visual Studio Code.
SOFTWARE REQUIREMENTS
REQUIREMENTS

3. 1. CPU: A modern multi-core processor with at least 8 cores, such as an Intel i7
or AMD Ryzen 7.
2. RAM: At least 8 GB of memory, preferably 64 GB or more, to handle large
datasets and multi-tasking.
3. GPU: A powerful graphics card with at least 8 GB of memory, preferably 11
GB or more, such as an NVIDIA RTX 3080 or higher, to accelerate deep
learning computations.
4. Storage: A fast solid-state drive (SSD) with at least 1 TB of capacity,
preferably 2 TB or more, to store datasets and trained models.
HARDWARE REQUIREMENTS